/ML_challenge

my contribution to ecometering challenge (datascience.net)

Primary LanguagePython

Modéliser les consommations électriques de sites B2B

My solution to the challenge Datascience.net:

Dependencies

  • pandas (pip install -U pandas)
  • numpy (pip install -U numpy)
  • sklearn (pip install -U sklearn)
  • (matplotlib)

Running and testing

To test and run the implementation, use the driver main:

./main --verbose --use_cache_data --use_cache_trainingset --test --plot --compute_reality

A file named str(time.time())+'_ML.csv' will be created in ./results/ if istest == False. Else, the training is not done on the last ten days, and a comparison with the true value is done. For testing purpose, running:

%run main

in IPython, and play around with matplotlib is the best solution.

TODO LIST

  • nothing